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Designing of loss function for 3d pedestrian detection using centernet
- Authors
- Kim, C.Y.; Lee, D.H.; Kim, H.J.; Memon, A.A.; Iqbal, E.; Choi, Kwang Nam
- Issue Date
- Dec-2020
- Publisher
- Association for Computing Machinery
- Keywords
- 3D Object Detection; Deep Learning; Monocular 3D Object Detection; Object Detection; Pedestrian Detection
- Citation
- ACM International Conference Proceeding Series, pp 5 - 10
- Pages
- 6
- Journal Title
- ACM International Conference Proceeding Series
- Start Page
- 5
- End Page
- 10
- ISSN
- 0000-0000
- Abstract
- Pedestrian detection has been a popular research topic in the last decade. In the past, anchor-based networks, for example, 1-stage and 2-stage detector, were famous for pedestrian detection. However, keypoint-based networks among anchor-free networks have been proposed recently and show high performance compared to anchor-based networks. CenterNet is a kind of keypoint-based network used for object detection. We modified the loss Function of CenterNet and proposed a weight function to train an object's height and width for 3D pedestrian detection. The evaluation of 3D pedestrian detection with the modified loss function is performed using the KITTI dataset's monocular images. The proposed loss function improves accuracy in the 3D pedestrian detection network compared to the original loss function. © 2020 ACM.
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